Explainable Artificial Intelligence (XAI) with Applications

This chapter begins by defining explainable artificial intelligence (XAI). The explainabilities of existing ML models are then compared, showing the need for XAI applications to improve the explainabilities of some ML models. To this end, XAI techniques and tools for interpreting and enhancing artificial intelligence applications, especially in the field of ambient intelligence (AmI), are discussed. Subsequently, the requirements for trustable AI and XAI are listed. Various classifications of existing XAI methods are then performed to meet these requirements. A literature analysis was also conducted on the application of XAI in various fields, showing that services, medicine, and education are the most common application fields of XAI. Finally, several types of explanations are introduced. XAI techniques for feature importance evaluation are also described.